Amazon Fraud Detector
Machine learning software
- Features
- Ease of use
- Ease of management
- Quality of support
- Affordability
- Market presence
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Pay-as-you-go
Free Trial
Free version unavailable
Small
Medium
Large
- Banking and insurance
- Retail and wholesale
- Accommodation and food services
What is Amazon Fraud Detector
Amazon Fraud Detector is a managed machine learning service for building, deploying, and operating fraud detection models. It targets teams that need to score events such as online payments, account creation, and login attempts using historical data and real-time signals. The service provides pre-built fraud-related model templates, feature transformations, and rule-based decision logic to combine model scores with business policies. It integrates with other AWS services for data ingestion, model hosting, and application deployment.
Managed fraud ML workflow
The service covers data preparation, model training, deployment, and real-time scoring within a managed AWS offering. It includes fraud-oriented constructs (events, entities, outcomes) that reduce the amount of custom plumbing required compared with general-purpose ML platforms. Teams can iterate on models and publish detectors without standing up separate model-serving infrastructure. This can shorten time-to-production for common fraud use cases.
Rules plus model scoring
Amazon Fraud Detector supports combining ML model outputs with rules to implement decision policies (for example, thresholds, allow/deny lists, and step-up verification). This helps organizations encode compliance or operational constraints that are difficult to capture purely in a statistical model. It also enables staged rollouts where rules provide guardrails while models mature. The approach supports practical fraud operations where deterministic controls and probabilistic scoring coexist.
AWS-native integration options
The product fits into AWS-centric architectures and can connect to common AWS data and application services for event ingestion and downstream actions. This reduces integration effort for organizations already standardizing on AWS identity, data, and compute services. It also benefits from AWS operational tooling for access control, monitoring, and deployment workflows. For many teams, this is simpler than integrating a standalone ML stack.
AWS lock-in considerations
The service is designed for AWS and typically works best when data pipelines and applications already run there. Organizations with multi-cloud or on-prem requirements may face additional integration work or duplicated tooling. Portability of detectors and surrounding workflows to non-AWS environments is limited. This can be a constraint for enterprises with strict vendor-neutral platform strategies.
Less flexible than ML platforms
Compared with general-purpose ML and analytics platforms, Amazon Fraud Detector provides a more opinionated workflow focused on fraud event scoring. Advanced customization of modeling approaches, feature engineering, and end-to-end experimentation may be constrained relative to building directly on broader ML frameworks. Teams with specialized fraud science requirements may need complementary tooling. This can increase overall system complexity for advanced programs.
Data quality and labeling burden
Model performance depends on having sufficient historical events and reliable outcome labels (for example, confirmed fraud vs. legitimate). Many organizations struggle with delayed chargeback signals, inconsistent case management outcomes, or sparse labels for new products. These issues can limit achievable accuracy and increase time spent on data governance. The service does not remove the need for ongoing monitoring and retraining processes.
Plan & Pricing
Pricing model: Pay-as-you-go Free tier/trial: 2-month free trial available (see notes)
Data processing & storage: $0.10 per GB
Model training & hosting:
- Model training: $0.39 per compute hour
- Model hosting: $0.06 per hour
Fraud predictions (per prediction, billed monthly in tiers):
- Online Fraud Insights:
- First 100,000 predictions/month: $0.0300 per prediction
- Over 100,000 predictions/month: $0.0075 per prediction
- Transaction Fraud Insights:
- First 100,000 predictions/month: $0.0300 per prediction
- Over 100,000 predictions/month: $0.0075 per prediction
- Rule-based Fraud Predictions:
- First 400,000 predictions/month: $0.00500 per prediction
- Next 800,000 predictions/month: $0.00250 per prediction
- Over 1,200,000 predictions/month: $0.00125 per prediction
- Account Takeover Insights:
- First 10,000,000 predictions/month: $0.0010 per prediction
- Next 90,000,000 predictions/month: $0.0005 per prediction
- Over 100,000,000 predictions/month: $0.0003 per prediction
Example pricing: (from AWS official pricing page — examples show combined charges for storage, training, hosting, and prediction usage)
Notes & key details:
- AWS states pricing is pay-as-you-go with no minimums or upfront commitments.
- Availability change notice on the official pricing page: "AWS Amazon Fraud Detector is no longer accepting new customers." AWS recommends alternatives (Amazon SageMaker, AutoGluon, AWS WAF).
- Free trial: AWS lists a 2-month free trial allocation (e.g., 50 training compute hours, up to 500 hosting hours, 20 GB event storage/month, 30,000 Online Fraud Insights predictions/month, 30,000 Transaction Fraud Insights predictions/month, 30,000 rules-based predictions/month, and 1,000,000 Account Takeover predictions for the first two months) on the official localized pricing page.
- Predictions that use a model imported from Amazon SageMaker are priced as rules-based predictions.
Seller details
Amazon Web Services, Inc.
Seattle, Washington, USA
2006
Subsidiary
https://aws.amazon.com/
https://x.com/awscloud
https://www.linkedin.com/company/amazon-web-services/
